Rising token counts and pull request throughput look impressive, but they miss where AI creates real value: preventing bad decisions and surfacing blockers before they cost time.

The tech giant's latest offering is a scaled-down version of its Exadata platform and helps differentiate its lineup of databases from those of competing providers.

Amid slow growth and a sliding stock price, the analytics provider is the latest specialist to join forces with a larger enterprise rather than remain independent.

Inference is the largest long-term expense in enterprise AI deployments. Businesses can keep AI inference costs in check with these seven optimization strategies.

In today's rapidly changing writing landscape -- just kidding. Most readers are savvy enough to recognize specific phrases as AI tells. Why does AI write like that, and why does it matter?

Integrating GenAI with VoIP can deliver cost savings and efficiency through automated call routing, real-time translation, advanced analytics and intelligent chatbots.

Native access to data lakes and a new data retrieval engine highlight the latest release as the vendor competes for market share amid a crowded field.

As enterprise AI deployments accelerate, businesses find workflow redesign, change management and employee trust, not just the tools, determine adoption success.

The database vendor is entering a crowded market with the launch of a database purpose-built for agents by unifying disparate data workloads.

Too often, policymakers base their organization's policies for AI usage on what they read in the news instead of first-hand experience working with the latest AI features.

Executives need AI knowledge to lead transformations effectively. These leading programs vary in depth and cost, and focus on strategy, governance and practical implementation.

While not the first AI governance suite, the vendor's Intelligence Operating System is somewhat unique by unifying data, context and agents in one environment.

Expanding AI use requires new operating models, talent and mindsets -- not just more resources. Companies fail because they try to scale experiments instead of redesigning systems.

Enterprises are expanding their use of automation in IT, where AI is changing the landscape with trends such as agentic workflows, automated decision-making and AI-driven pipelines.

New capabilities, such as data quality agents and a feature that makes data products more reusable, support engineers to help organizations more easily achieve their AI goals.

When building ML models, developers can use several techniques to make models easier for humans to interpret, leading to improved transparency, troubleshooting and user acceptance.

AI workloads are straining data center power and grid capacity, forcing enterprises to rethink workload, cloud and infrastructure strategies beyond traditional approaches.

The bottleneck in enterprise AI has shifted from model access to deployment. Vendors are responding by embedding engineering teams directly inside customer environments.

Generate Biomedicines has been AI-native since its founding in 2018. Hear from co-founder and CTO Gevorg Grigoryan about how the biotech company uses AI to its advantage.

Machine learning challenges cover everything from ethical issues to data quality and user acceptance concerns. Learn about seven common obstacles.